24、Flink 的table api与sql之Catalogs（java api操作视图）-3

一瓢一瓢的饮 alanchan2023-10-19 13:42

1、Flink 部署、概念介绍、source、transformation、sink使用示例、四大基石介绍和示例等系列综合文章链接

13、Flink 的table api与sql的基本概念、通用api介绍及入门示例
14、Flink 的table api与sql之数据类型: 内置数据类型以及它们的属性
15、Flink 的table api与sql之流式概念-详解的介绍了动态表、时间属性配置（如何处理更新结果）、时态表、流上的join、流上的确定性以及查询配置
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及FileSystem示例（1）
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及Elasticsearch示例（2）
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及Apache Kafka示例（3）
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及JDBC示例（4）

16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及Apache Hive示例（6）

20、Flink SQL之SQL Client: 不用编写代码就可以尝试 Flink SQL，可以直接提交 SQL 任务到集群上

22、Flink 的table api与sql之创建表的DDL
24、Flink 的table api与sql之Catalogs（介绍、类型、java api和sql实现ddl、java api和sql操作catalog）-1
24、Flink 的table api与sql之Catalogs（java api操作数据库、表）-2
24、Flink 的table api与sql之Catalogs（java api操作视图）-3

26、Flink 的SQL之概览与入门示例
27、Flink 的SQL之SELECT (select、where、distinct、order by、limit、集合操作和去重)介绍及详细示例（1）
27、Flink 的SQL之SELECT (SQL Hints 和 Joins)介绍及详细示例（2）
27、Flink 的SQL之SELECT (窗口函数)介绍及详细示例（3）
27、Flink 的SQL之SELECT (窗口聚合)介绍及详细示例（4）
27、Flink 的SQL之SELECT (Group Aggregation分组聚合、Over Aggregation Over聚合 和 Window Join 窗口关联)介绍及详细示例（5）
27、Flink 的SQL之SELECT (Top-N、Window Top-N 窗口 Top-N 和 Window Deduplication 窗口去重)介绍及详细示例（6）
27、Flink 的SQL之SELECT (Pattern Recognition 模式检测)介绍及详细示例（7）

29、Flink SQL之DESCRIBE、EXPLAIN、USE、SHOW、LOAD、UNLOAD、SET、RESET、JAR、JOB Statements、UPDATE、DELETE（1）
29、Flink SQL之DESCRIBE、EXPLAIN、USE、SHOW、LOAD、UNLOAD、SET、RESET、JAR、JOB Statements、UPDATE、DELETE（2）
30、Flink SQL之SQL 客户端（通过kafka和filesystem的例子介绍了配置文件使用-表、视图等）
32、Flink table api和SQL 之用户自定义 Sources & Sinks实现及详细示例
41、Flink之Hive 方言介绍及详细示例
42、Flink 的table api与sql之Hive Catalog
43、Flink之Hive 读写及详细验证示例
44、Flink之module模块介绍及使用示例和Flink SQL使用hive内置函数及自定义函数详细示例--网上有些说法好像是错误的

文章目录

本文简单介绍了通过java api操作视图，提供了三个示例，即sql实现和java api的两种实现方式。

本文依赖flink和hive、hadoop集群能正常使用。

本文示例java api的实现是通过Flink 1.13.5版本做的示例，SQL 如果没有特别说明则是Flink 1.17版本。

五、Catalog API

3、视图操作

1）、官方示例

java 复制代码 
// create view
catalog.createTable(new ObjectPath("mydb", "myview"), new CatalogViewImpl(...), false);

// drop view
catalog.dropTable(new ObjectPath("mydb", "myview"), false);

// alter view
catalog.alterTable(new ObjectPath("mydb", "mytable"), new CatalogViewImpl(...), false);

// rename view
catalog.renameTable(new ObjectPath("mydb", "myview"), "my_new_view", false);

// get view
catalog.getTable("myview");

// check if a view exist or not
catalog.tableExists("mytable");

// list views in a database
catalog.listViews("mydb");

2）、SQL创建HIVE 视图示例

1、maven依赖
xml 复制代码 
properties>
		<encoding>UTF-8</encoding>
		<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
		<maven.compiler.source>1.8</maven.compiler.source>
		<maven.compiler.target>1.8</maven.compiler.target>
		<java.version>1.8</java.version>
		<scala.version>2.12</scala.version>
		<flink.version>1.13.6</flink.version>
	</properties>

	<dependencies>
		<dependency>
			<groupId>org.apache.flink</groupId>
			<artifactId>flink-clients_2.11</artifactId>
			<version>${flink.version}</version>
		</dependency>
		<dependency>
			<groupId>org.apache.flink</groupId>
			<artifactId>flink-scala_2.11</artifactId>
			<version>${flink.version}</version>
		</dependency>
		<dependency>
			<groupId>org.apache.flink</groupId>
			<artifactId>flink-java</artifactId>
			<version>${flink.version}</version>
			<scope>provided</scope> 
		</dependency>
		<dependency>
			<groupId>org.apache.flink</groupId>
			<artifactId>flink-streaming-scala_2.11</artifactId>
			<version>${flink.version}</version>
		</dependency>
		<dependency>
			<groupId>org.apache.flink</groupId>
			<artifactId>flink-streaming-java_2.11</artifactId>
			<version>${flink.version}</version>
			<scope>provided</scope>
		</dependency>
		<dependency>
			<groupId>org.apache.flink</groupId>
			<artifactId>flink-table-api-scala-bridge_2.11</artifactId>
			<version>${flink.version}</version>
		</dependency>
		<dependency>
			<groupId>org.apache.flink</groupId>
			<artifactId>flink-table-api-java-bridge_2.11</artifactId>
			<version>${flink.version}</version>
		</dependency>
		<!-- blink执行计划,1.11+默认的 -->
		<dependency>
			<groupId>org.apache.flink</groupId>
			<artifactId>flink-table-planner-blink_2.11</artifactId>
			<version>${flink.version}</version>
			<scope>provided</scope> 
		</dependency>
		<dependency>
			<groupId>org.apache.flink</groupId>
			<artifactId>flink-table-common</artifactId>
			<version>${flink.version}</version>
		</dependency>
		<!-- flink连接器 -->
		<dependency>
			<groupId>org.apache.flink</groupId>
			<artifactId>flink-connector-kafka_2.12</artifactId>
			<version>${flink.version}</version>
			<!-- <scope>provided</scope> -->
		</dependency>
		<dependency>
			<groupId>org.apache.flink</groupId>
			<artifactId>flink-sql-connector-kafka_2.12</artifactId>
			<version>${flink.version}</version>
			<scope>provided</scope>
		</dependency>
		<dependency>
			<groupId>org.apache.flink</groupId>
			<artifactId>flink-connector-jdbc_2.12</artifactId>
			<version>${flink.version}</version>
			<scope>provided</scope>
		</dependency>
		<dependency>
			<groupId>org.apache.flink</groupId>
			<artifactId>flink-csv</artifactId>
			<version>${flink.version}</version>
		</dependency>
		<dependency>
			<groupId>org.apache.flink</groupId>
			<artifactId>flink-json</artifactId>
			<version>${flink.version}</version>
		</dependency>

		<dependency>
			<groupId>org.apache.flink</groupId>
			<artifactId>flink-connector-hive_2.12</artifactId>
			<version>${flink.version}</version>
			<scope>provided</scope> 
		</dependency>
		<dependency>
			<groupId>org.apache.hive</groupId>
			<artifactId>hive-metastore</artifactId>
			<version>2.1.0</version>
		</dependency>
		<dependency>
			<groupId>org.apache.hive</groupId>
			<artifactId>hive-exec</artifactId>
			<version>3.1.2</version>
			<scope>provided</scope> 
		</dependency>

		<dependency>
			<groupId>org.apache.flink</groupId>
			<artifactId>flink-shaded-hadoop-2-uber</artifactId>
			<version>2.7.5-10.0</version>
			<!-- <scope>provided</scope> -->
		</dependency>

		<dependency>
			<groupId>mysql</groupId>
			<artifactId>mysql-connector-java</artifactId>
			<version>5.1.38</version>
			<scope>provided</scope>
			<!--<version>8.0.20</version> -->
		</dependency>

		<!-- 日志 -->
		<dependency>
			<groupId>org.slf4j</groupId>
			<artifactId>slf4j-log4j12</artifactId>
			<version>1.7.7</version>
			<scope>runtime</scope>
		</dependency>
		<dependency>
			<groupId>log4j</groupId>
			<artifactId>log4j</artifactId>
			<version>1.2.17</version>
			<scope>runtime</scope>
		</dependency>

		<dependency>
			<groupId>com.alibaba</groupId>
			<artifactId>fastjson</artifactId>
			<version>1.2.44</version>
		</dependency>

		<dependency>
			<groupId>org.projectlombok</groupId>
			<artifactId>lombok</artifactId>
			<version>1.18.2</version>
			<!-- <scope>provided</scope> -->
		</dependency>

	</dependencies>

	<build>
		<sourceDirectory>src/main/java</sourceDirectory>
		<plugins>
			<!-- 编译插件 -->
			<plugin>
				<groupId>org.apache.maven.plugins</groupId>
				<artifactId>maven-compiler-plugin</artifactId>
				<version>3.5.1</version>
				<configuration>
					<source>1.8</source>
					<target>1.8</target>
					<!--<encoding>${project.build.sourceEncoding}</encoding> -->
				</configuration>
			</plugin>
			<plugin>
				<groupId>org.apache.maven.plugins</groupId>
				<artifactId>maven-surefire-plugin</artifactId>
				<version>2.18.1</version>
				<configuration>
					<useFile>false</useFile>
					<disableXmlReport>true</disableXmlReport>
					<includes>
						<include>**/*Test.*</include>
						<include>**/*Suite.*</include>
					</includes>
				</configuration>
			</plugin>
			<!-- 打包插件(会包含所有依赖) -->
			<plugin>
				<groupId>org.apache.maven.plugins</groupId>
				<artifactId>maven-shade-plugin</artifactId>
				<version>2.3</version>
				<executions>
					<execution>
						<phase>package</phase>
						<goals>
							<goal>shade</goal>
						</goals>
						<configuration>
							<filters>
								<filter>
									<artifact>*:*</artifact>
									<excludes>
										<!-- zip -d learn_spark.jar META-INF/*.RSA META-INF/*.DSA META-INF/*.SF -->
										<exclude>META-INF/*.SF</exclude>
										<exclude>META-INF/*.DSA</exclude>
										<exclude>META-INF/*.RSA</exclude>
									</excludes>
								</filter>
							</filters>
							<transformers>
								<transformer
									implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
									<!-- 设置jar包的入口类(可选) -->
									<mainClass> org.table_sql.TestHiveViewBySQLDemo</mainClass>
								</transformer>
							</transformers>
						</configuration>
					</execution>
				</executions>
			</plugin>
		</plugins>
	</build>
2、代码
java 复制代码 
package org.table_sql;

import java.util.HashMap;
import java.util.List;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.SqlDialect;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.catalog.CatalogDatabaseImpl;
import org.apache.flink.table.catalog.CatalogView;
import org.apache.flink.table.catalog.ObjectPath;
import org.apache.flink.table.catalog.hive.HiveCatalog;
import org.apache.flink.table.module.hive.HiveModule;
import org.apache.flink.types.Row;
import org.apache.flink.util.CollectionUtil;

/**
 * @author alanchan
 *
 */
public class TestHiveViewBySQLDemo {
	public static final String tableName = "viewtest";
	public static final String hive_create_table_sql = "CREATE  TABLE  " + tableName +  " (\n" + 
			  "  id INT,\n" + 
			  "  name STRING,\n" + 
			  "  age INT" + ") " + 
			  "TBLPROPERTIES (\n" + 
			  "  'sink.partition-commit.delay'='5 s',\n" + 
			  "  'sink.partition-commit.trigger'='partition-time',\n" + 
			  "  'sink.partition-commit.policy.kind'='metastore,success-file'" + ")";

	/**
	 * @param args
	 * @throws Exception
	 */
	public static void main(String[] args) throws Exception {
		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
		StreamTableEnvironment tenv = StreamTableEnvironment.create(env);

		String moduleName = "myhive";
		String hiveVersion = "3.1.2";
		tenv.loadModule(moduleName, new HiveModule(hiveVersion));

		String name = "alan_hive";
		String defaultDatabase = "default";
		String databaseName = "viewtest_db";
		String hiveConfDir = "/usr/local/bigdata/apache-hive-3.1.2-bin/conf";

		HiveCatalog hiveCatalog = new HiveCatalog(name, defaultDatabase, hiveConfDir);
		tenv.registerCatalog(name, hiveCatalog);
		tenv.useCatalog(name);
		tenv.listDatabases();
		hiveCatalog.createDatabase(databaseName, new CatalogDatabaseImpl(new HashMap(), hiveConfDir) {
		}, true);

//		tenv.executeSql("create database "+databaseName);
		tenv.useDatabase(databaseName);

		// 创建第一个视图viewName_byTable
		String selectSQL = "select * from " + tableName;
		String viewName_byTable = "test_view_table_V";
		String createViewSQL = "create view " + viewName_byTable + " as " + selectSQL;

		tenv.getConfig().setSqlDialect(SqlDialect.HIVE);
		tenv.executeSql(hive_create_table_sql);

//		tenv.getConfig().setSqlDialect(SqlDialect.DEFAULT);

		String insertSQL = "insert into " + tableName + " values (1,'alan',18)";
		tenv.executeSql(insertSQL);

		tenv.executeSql(createViewSQL);
		tenv.listViews();

		CatalogView catalogView = (CatalogView) hiveCatalog.getTable(new ObjectPath(databaseName, viewName_byTable));

		List<Row> results = CollectionUtil.iteratorToList(tenv.executeSql("select * from " + viewName_byTable).collect());

		for (Row row : results) {
			System.out.println("test_view_table_V: " + row.toString());
		}

		// 创建第二个视图
		String viewName_byView = "test_view_view_V";
		tenv.executeSql("create view " + viewName_byView + " (v2_id,v2_name,v2_age) comment 'test_view_view_V comment' as select * from " + viewName_byTable);
		catalogView = (CatalogView) hiveCatalog.getTable(new ObjectPath(databaseName, viewName_byView));

		results = CollectionUtil.iteratorToList(tenv.executeSql("select * from " + viewName_byView).collect());
		System.out.println("test_view_view_V comment : " + catalogView.getComment());

		for (Row row : results) {
			System.out.println("test_view_view_V : " + row.toString());
		}
		tenv.executeSql("drop database " + databaseName + " cascade");
	}

}
3、运行结果

前提是flink的集群可用。使用maven打包成jar。

bash 复制代码 
[alanchan@server2 bin]$ flink run  /usr/local/bigdata/flink-1.13.5/examples/table/table_sql-0.0.2-SNAPSHOT.jar

Hive Session ID = ed6d5c9b-e00f-4881-840d-24c72aba6db7
Hive Session ID = 14445dc8-1f08-4f0f-bb45-aba8c6f52174
Job has been submitted with JobID bff7b59367bd5de6e778b442c4cc4404
Hive Session ID = 4c16f4fc-4c10-4353-b322-e6633e3ebe3d
Hive Session ID = 57949f09-bdcb-497f-a85c-ed9766fc4ce3
2023-10-13 02:42:24,891 INFO  org.apache.hadoop.mapred.FileInputFormat                     [] - Total input files to process : 0
Job has been submitted with JobID 80e48bb76e3d580412fdcdc434a8a979
test_view_table_V: +I[1, alan, 18]
Hive Session ID = a73d5b93-2129-4159-ad5e-0814df77e987
Hive Session ID = e4ae1a79-4d5e-4835-81de-ebc2041eedf9
2023-10-13 02:42:33,648 INFO  org.apache.hadoop.mapred.FileInputFormat                     [] - Total input files to process : 1
Job has been submitted with JobID c228d9ce3bdce91dc68bff75d14db1e5
test_view_view_V comment : test_view_view_V comment
test_view_view_V : +I[1, alan, 18]
Hive Session ID = e4a38393-d760-4bd3-8d8b-864cbe0daba7

3）、API创建Hive 视图示例

通过api创建视图相对比较麻烦，且存在版本更新的过期方法情况。

通过TableSchema和CatalogViewImpl创建视图则已过期，当前推荐使用通过CatalogView和ResolvedSchema来创建视图。

另外需要注意的是下面两个参数的区别

String originalQuery,原始的sql

String expandedQuery,带有数据库名称的表，甚至包含hivecatalog

例如：如果使用default作为默认的数据库，查询语句为select * from test1，则

originalQuery = "select name,value from test1"即可，

expandedQuery = "selecttest1.name, test1.value from default.test1"

修改、删除视图等操作比较简单，不再赘述。

1、maven依赖

此处使用的依赖与上示例一致，mainclass变成本示例的类，不再赘述。

2、代码
java 复制代码 
import static org.apache.flink.util.Preconditions.checkNotNull;

import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.DataTypes;
import org.apache.flink.table.api.Schema;
import org.apache.flink.table.api.SqlDialect;
import org.apache.flink.table.api.TableSchema;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.catalog.CatalogBaseTable;
import org.apache.flink.table.catalog.CatalogDatabaseImpl;
import org.apache.flink.table.catalog.CatalogView;
import org.apache.flink.table.catalog.CatalogViewImpl;
import org.apache.flink.table.catalog.ObjectPath;
import org.apache.flink.table.catalog.ResolvedCatalogView;
import org.apache.flink.table.catalog.ResolvedSchema;
import org.apache.flink.table.catalog.exceptions.CatalogException;
import org.apache.flink.table.catalog.exceptions.DatabaseNotExistException;
import org.apache.flink.table.catalog.exceptions.TableAlreadyExistException;
import org.apache.flink.table.catalog.hive.HiveCatalog;
import org.apache.flink.table.module.hive.HiveModule;
import org.apache.flink.types.Row;
import org.apache.flink.util.CollectionUtil;
import org.apache.flink.table.catalog.CatalogBaseTable;
import org.apache.flink.table.catalog.Column;

/**
 * @author alanchan
 *
 */
public class TestHiveViewByAPIDemo {
	public static final String tableName = "viewtest";
	public static final String hive_create_table_sql = "CREATE  TABLE  " + tableName +  " (\n" + 
			  "  id INT,\n" + 
			  "  name STRING,\n" + 
			  "  age INT" + ") " + 
			  "TBLPROPERTIES (\n" + 
			  "  'sink.partition-commit.delay'='5 s',\n" + 
			  "  'sink.partition-commit.trigger'='partition-time',\n" + 
			  "  'sink.partition-commit.policy.kind'='metastore,success-file'" + ")";

	/**
	 * @param args
	 * @throws Exception
	 */
	public static void main(String[] args) throws Exception {
		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
		StreamTableEnvironment tenv = StreamTableEnvironment.create(env);
		System.setProperty("HADOOP_USER_NAME", "alanchan");
		String moduleName = "myhive";
		String hiveVersion = "3.1.2";
		tenv.loadModule(moduleName, new HiveModule(hiveVersion));

		String catalogName = "alan_hive";
		String defaultDatabase = "default";
		String databaseName = "viewtest_db";
		String hiveConfDir = "/usr/local/bigdata/apache-hive-3.1.2-bin/conf";

		HiveCatalog hiveCatalog = new HiveCatalog(catalogName, defaultDatabase, hiveConfDir);
		tenv.registerCatalog(catalogName, hiveCatalog);
		tenv.useCatalog(catalogName);
		tenv.listDatabases();
		
		hiveCatalog.createDatabase(databaseName, new CatalogDatabaseImpl(new HashMap(), hiveConfDir) {
		}, true);

//		tenv.executeSql("create database "+databaseName);
		tenv.useDatabase(databaseName);

		tenv.getConfig().setSqlDialect(SqlDialect.HIVE);
		tenv.executeSql(hive_create_table_sql);
		String insertSQL = "insert into " + tableName + " values (1,'alan',18)";
		String insertSQL2 = "insert into " + tableName + " values (2,'alan2',19)";
		String insertSQL3 = "insert into " + tableName + " values (3,'alan3',20)";
		tenv.executeSql(insertSQL);
		tenv.executeSql(insertSQL2);
		tenv.executeSql(insertSQL3);
		
		tenv.getConfig().setSqlDialect(SqlDialect.DEFAULT);
		String viewName1 = "test_view_table_V";
		String viewName2 = "test_view_table_V2";
		
		ObjectPath path1= new ObjectPath(databaseName, viewName1);
		//ObjectPath.fromString("viewtest_db.test_view_table_V2")
		ObjectPath path2= new ObjectPath(databaseName, viewName2);
		
		String originalQuery = "SELECT id, name, age FROM "+tableName+" WHERE id >=1 ";
//		String originalQuery = String.format("select * from %s",tableName+" WHERE id >=1 ");
		System.out.println("originalQuery:"+originalQuery);
		String expandedQuery = "SELECT  id, name, age FROM "+databaseName+"."+tableName+"  WHERE id >=1 ";		
//		String expandedQuery = String.format("select * from %s.%s", catalogName, path1.getFullName());
		System.out.println("expandedQuery:"+expandedQuery);
		String comment = "this is a comment";
		
		// 创建视图，第一种方式(通过TableSchema和CatalogViewImpl)，已声明过期	
		createView1(originalQuery,expandedQuery,comment,hiveCatalog,path1);
		// 查询视图
		List<Row> results = CollectionUtil.iteratorToList( tenv.executeSql("select * from " + viewName1).collect());
		for (Row row : results) {
			System.out.println("test_view_table_V: " + row.toString());
		}
		
		// 创建视图，第二种方式（通过Schema和ResolvedSchema）
		createView2(originalQuery,expandedQuery,comment,hiveCatalog,path2);
		
		List<Row> results2 = CollectionUtil.iteratorToList( tenv.executeSql("select * from viewtest_db.test_view_table_V2").collect());
		for (Row row : results2) {
			System.out.println("test_view_table_V2: " + row.toString());
		}

		tenv.executeSql("drop database " + databaseName + " cascade");
	}
	
	static void createView1(String originalQuery,String expandedQuery,String comment,HiveCatalog hiveCatalog,ObjectPath path) throws Exception {
		TableSchema viewSchema = new TableSchema(new String[]{"id", "name","age"}, new TypeInformation[]{Types.INT, Types.STRING,Types.INT});
		CatalogBaseTable viewTable = new CatalogViewImpl(
				originalQuery,
				expandedQuery,
				viewSchema, 
				new HashMap(),
				comment);
		hiveCatalog.createTable(path, viewTable, false);
	}
	
	static void createView2(String originalQuery,String expandedQuery,String comment,HiveCatalog hiveCatalog,ObjectPath path) throws Exception {
		ResolvedSchema resolvedSchema = new ResolvedSchema(
                Arrays.asList(
                        Column.physical("id", DataTypes.INT()),
                        Column.physical("name", DataTypes.STRING()),
                        Column.physical("age", DataTypes.INT())),
                Collections.emptyList(),
                null);
		
		 CatalogView origin =  CatalogView.of(
	                        Schema.newBuilder().fromResolvedSchema(resolvedSchema).build(),
	                        comment,
//	                        String.format("select * from tt"),
//	                        String.format("select * from %s.%s", TEST_CATALOG_NAME, path1.getFullName()),
	                        originalQuery,
	                        expandedQuery,
	                        Collections.emptyMap());
			CatalogView view = new ResolvedCatalogView(origin, resolvedSchema);
//			ObjectPath.fromString("viewtest_db.test_view_table_V2")
		hiveCatalog.createTable(path, view, false);
		
	}

}
3、运行结果
bash 复制代码 
[alanchan@server2 bin]$ flink run  /usr/local/bigdata/flink-1.13.5/examples/table/table_sql-0.0.3-SNAPSHOT.jar

Hive Session ID = ab4d159a-b2d3-489e-988f-eebdc43d9517
Hive Session ID = 391de19c-5d5a-4a83-a88c-c43cca71fc63
Job has been submitted with JobID a880510032165523f3f2a559c5ab4ec9
Hive Session ID = cb063c31-eaf2-44e3-8fc0-9e8d2a6a3a5d
Job has been submitted with JobID cb05286c404b561306f8eb3969c3456a
Hive Session ID = 8132b36e-c9e2-41a2-8f42-3fe842e0991f
Job has been submitted with JobID 264aef7da1b17598bda159d946827dea
Hive Session ID = 7657be14-8188-4362-84a9-4c84c596021b
2023-10-16 07:21:19,073 INFO  org.apache.hadoop.mapred.FileInputFormat                     [] - Total input files to process : 3
Job has been submitted with JobID 05c2bb7265b0430cb12e00237f18444b
test_view_table_V: +I[1, alan, 18]
test_view_table_V: +I[2, alan2, 19]
test_view_table_V: +I[3, alan3, 20]
Hive Session ID = 7bb01c0d-03c9-413a-9040-c89676cec3b9
2023-10-16 07:21:27,512 INFO  org.apache.hadoop.mapred.FileInputFormat                     [] - Total input files to process : 3
Job has been submitted with JobID 79130d1fe56d88a784980d16e7f1cfb4
test_view_table_V2: +I[1, alan, 18]
test_view_table_V2: +I[2, alan2, 19]
test_view_table_V2: +I[3, alan3, 20]
Hive Session ID = 6d44ea95-f733-4c56-8da4-e2687a4bf945

本文简单介绍了通过java api操作视图，提供了三个示例，即sql实现和java api的两种实现方式。

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